System and method for modifying a nutrition requirement

ABSTRACT

A system for modifying a nutrition requirement includes a computing device configured to obtain a user attribute, identify a nutrition requirement as a function of the user attribute, wherein identifying further comprises, receiving a nutrition training set, wherein the nutrition training set relates an aliment to a user attribute, and identifying the nutrition requirement as a function of a nutrition machine-learning process, receive a monitoring element from a monitoring device, modify the nutrition requirement, wherein modifying further comprises, receiving a modification training set, wherein the modification training set relates a monitoring element to a nutrition outcome, and modifying the nutrition requirement as a function of a modification machine-learning process, identify an aliment that fulfills the modified user nutrition requirement, and present an aliment on a display device.

FIELD OF THE INVENTION

The present invention generally relates to the field of artificialintelligence. In particular, the present invention is directed to asystem and method for modifying nutrition requirements.

BACKGROUND

Nutrition requirements are universal for individuals, resulting in poorresults for individuals and frustrating individuals. This is furthercomplicated by the overwhelming source of nutrition requirements furtherconfusing and frustrating individuals. The lack of personalizednutrition requirements in the current consumer market has resulted inindividuals that fail to obtain the positive results that are possible.

SUMMARY OF THE DISCLOSURE

In an aspect a system for modifying a nutrition requirement includes acomputing device, the computing device configured to receive a userattribute, identify a nutrition requirement as a function of the userattribute, wherein identifying further comprises receiving a nutritiontraining set, the nutrition training set relating at least an alimentand at least a user attribute and using a nutrition machine-learningprocess, wherein the nutrition machine-learning process is configuredusing the nutrition training set, receive a monitoring element from amonitoring device, modify the nutrition requirement as a function of themonitoring element, wherein modifying further comprises receiving amodification training set, wherein the modification training set relatesa monitoring element and a nutrition outcome, and using a modificationmachine-learning process, wherein the modification machine-learningprocess is configured using the modification training set, identify analiment that fulfills the modified user nutrition requirement, andpresent the aliment on a display device.

In another aspect a method for modifying a nutrition requirementincludes receiving, by a computing device, a user attribute,identifying, by the computing device, a nutrition requirement as afunction of the user attribute, wherein identifying further comprisesreceiving a nutrition training set, the nutrition training set relatingat least an aliment and at least a user attribute and using a nutritionmachine-learning process, wherein the nutrition machine-learning processis configured using the nutrition training set, receiving, by thecomputing device, a monitoring element from a monitoring device,modifying, by the computing device, the nutrition requirement as afunction of the monitoring element, wherein modifying further comprisesreceiving a modification training set, wherein the modification trainingset relates a monitoring element and a nutrition outcome, and using amodification machine-learning process, wherein the modificationmachine-learning process is configured using the modification trainingset, identifying, by the computing device, an ailment that fulfills themodified user nutrition requirement, and presenting, by the computingdevice, the aliment on a display device.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of asystem for modifying a nutrition requirement;

FIG. 2 is a block diagram of an exemplary embodiment of amachine-learning module;

FIG. 3 is a block diagram of an exemplary embodiment of a user ediblehistory database;

FIG. 4 is a schematic diagram of a system for modifying a nutritionrequirement using a monitoring device;

FIG. 5 is process flow diagram illustrating an exemplary embodiment of amethod of modifying a nutrition requirement; and

FIG. 6 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations, and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for modifying a nutrition requirement. In anembodiment this system modifies the nutrition requirement as a functionof a user attribute and a monitoring element. Aspects of the presentdisclosure can be used to modify the nutrition requirement that at leastenhances a user attribute as a function of the monitoring element. Thisis so at least in part, because the system obtains a user attribute fromthe user, identifies a nutrition requirement, and modifies the nutritionrequirement as a function of a monitoring element obtained from amonitoring device. In an embodiment, B. Exemplary embodimentsillustrating aspects of the present disclosure are described below inthe context of several specific examples.

Referring now to FIG. 1, an exemplary embodiment of a system 100 formodifying a nutrition requirement is illustrated. System includes acomputing device 104. Computing device 104 may include any computingdevice as described in this disclosure, including without limitation amicrocontroller, microprocessor, digital signal processor (DSP) and/orsystem on a chip (SoC) as described in this disclosure. Computing devicemay include, be included in, and/or communicate with a mobile devicesuch as a mobile telephone or smartphone. Computing device 104 mayinclude a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. Computing device 104 may interface or communicate with one ormore additional devices as described below in further detail via anetwork interface device. Network interface device may be utilized forconnecting computing device 104 to one or more of a variety of networks,and one or more devices. Examples of a network interface device include,but are not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A networkmay employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, softwareetc.) may be communicated to and/or from a computer and/or a computingdevice. Computing device 104 may include but is not limited to, forexample, a computing device or cluster of computing devices in a firstlocation and a second computing device or cluster of computing devicesin a second location. Computing device 104 may include one or morecomputing devices dedicated to data storage, security, distribution oftraffic for load balancing, and the like. Computing device 104 maydistribute one or more computing tasks as described below across aplurality of computing devices of computing device, which may operate inparallel, in series, redundantly, or in any other manner used fordistribution of tasks or memory between computing devices. Computingdevice 104 may be implemented using a “shared nothing” architecture inwhich data is cached at the worker, in an embodiment, this may enablescalability of system 100 and/or computing device.

Computing device 104 may be designed and/or configured to perform anymethod, method step, or sequence of method steps in any embodimentdescribed in this disclosure, in any order and with any degree ofrepetition. For instance, computing device 104 may be configured toperform a single step or sequence repeatedly until a desired orcommanded outcome is achieved; repetition of a step or a sequence ofsteps may be performed iteratively and/or recursively using outputs ofprevious repetitions as inputs to subsequent repetitions, aggregatinginputs and/or outputs of repetitions to produce an aggregate result,reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Computing device 104 mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Still referring to FIG. 1, computing device 104 is configured to receivea user attribute 108. As used in this disclosure a “user attribute”relates to a characteristic uniquely belonging to a user. User attribute108 may include, without limitation, particular traits, qualities,behaviors, and/or habits relating to a user. User attribute 108 may becomprised of a medical record, vigor status, and/or health qualifier. Asused in this disclosure “vigor status” relates to a qualitative measureof a user health. User vigor status may include without limitation, auser affliction, a user fitness status, a user wellness goal, a usermedical goal, or the like thereof. As used in this disclosure a “useraffliction” is a list or collection of current or potential ailmentsand/or diseases, and/or precursor states to such ailments and/ordiseases, including but not limited to physical, spiritual, and/orpsychological ailments and/or diseases correlating to any resultingimpact on the user. In an embodiment a physical ailment or disease mayinclude, without limitation, Influenza, Rhinovirus, Obesity, COVID-19,EEE, CRE, Ebola, Enterovirus D68, Influenza, Hantavirus, Hepatitis A,Hepatitis A, HIV/AIDS, Diabetes (Type I or Type II), Multiple Sclerosis,Chron's Disease, Colitis, Lupus, Rheumatoid Arthritis, Allergies,Asthma, Relapsing Polychondritis, Scleroderma, Liver Disease, HeartDisease, Cancer, and the like thereof. In an embodiment a spiritualailment or disease may include, without limitation, religious conflicts,chakra blockages, existential crisis, or the like thereof. In anembodiment a psychological ailment or disease may include, withoutlimitation, Alzheimer's, Parkinson's, alcohol or substance abusedisorder, anxiety disorder, ADD, ADHD, bipolar disorder, depression,eating disorder, obsessive-compulsive disorder, opioid use disorder,PTSD, schizophrenia, depersonalization disorder, dissociative amnesiaand/or fatigue, anorexia, bulimia, sleep disorders, wake disorders,paraphilic disorders, sexual disorders, child mental disorders,personality disorders, gender dysphoria, depression, and the likethereof. As used in this disclosure a “user fitness status” is anenumeration vector relating a user fitness to a fitness capability. Forexample, and without limitation, a user fitness status may indicate auser to have a low fitness status, wherein a low fitness statusindicates the user to be below average for fitness levels. As used inthis disclosure a “user wellness goal” is a set value or metric that auser would like to achieve relating to the user's wellness. For example,and without limitation, a user wellness goal may include increasedsleep, enhanced meditation, increase positivity, or the like thereof. Asused in this disclosure a “user medical goal” is a set value or metricthat a user and/or physician would like the user to achieve to increaseoverall medical health. For example, and without limitation, a usermedical goal may include decrease LDL, lower blood pressure, reducedheart rate, increased lung capacity, increased metabolic rate, or thelike thereof. As used in this disclosure a user “health qualifier” is apre-existing limiting medical or psychological concern. For example, ahealth qualifier may include a psychological barrier, wherein thepsychological barrier is preventing a user from performing a specificaction or task.

Still referring to FIG. 1, user attribute 108 may further be comprisedof comprised of a user edible history. As used in this disclosure a“user aliment history” is a history of previous aliment selections madeby the user. For example, and without limitation, user aliment historymay include aliment habits, aliments selected as a function of fitnessrelated activities, aliment preference, calories consumed, and the likethereof. As used in this disclosure “aliment habits” are aliments thatare frequently selected by the user. For example, and withoutlimitation, a user may frequently select bacon and eggs as a preferencefor a breakfast aliment. Aliments selected as a function of fitnessactivities may include, without limitation, elevated protein aliments,energy supplements, fat-burning supplements, and the like thereof. Asused in this disclosure “aliment preference” is a user wish, urge, want,and/or partiality towards a specific aliment. For example, a user mayhave an aliment preference of sweet aliments as opposed to souraliments. A user aliment history may include inputs from a food servicedatabank. As used in this disclosure “food service datastore” is adatastore relating the user to previously purchase aliments. Forexample, and without limitation, a food service databank may include thedatabase GRUBHUB service as provided by GrubHub of Chicago, Ill., thedatabase UBEREATS service as provided by UberEats of San Francisco,Calif., the database RESTOLABS service as provided by RestoLabs of Reno,Nev., the database 9FOLD service as provided by 9Fold Software of NewYork, N.Y., the database MENUDRIVE service as provided by MenuDrive ofAlbuquerque, N. Mex., the database SKIP THE COMMISION service asprovided by Skipthecommission Online Ordering Systems of Toronto,Canada, the database GLORIA FOOD service as provide by GloriaFood ofBucharest, Romania, the database IMENU360 service as provided byiMenu360 of Winnetka, Ill., the database RESTROAPP service as providedby RestroApp of Iselin, N.J., the database ORDERING.CO service asprovided by Ordering.co Berlin, Germany, the database CLOUD WAITRESSservice as provided by Cloud Waitress of Sydney, Australia, the databaseHELLOFRESH service as provided by HelloFresh of Berlin, Germany, thedatabase BLUE APRON service provided by Blue Apron of New York, N.Y.,The database TRIFECTA NUTRITION service as provided by TrifectaNutrition of Sacramento, Calif., the database FRESHLY service asprovided by Freshly of New York, N.Y., the database GREEN CHEF serviceas provided by Green Chef of Boulder, Colo., the database PURPLE CARROTas provided by Purple Carrot of Needham, Mass., the database PEACH DISHas provided by Peach Dish of Atlanta, Ga., the database PLATED serviceas provided by Plated of New York, N.Y., the database service of HOMECHEF as provided by Home Chef of Chicago, Ill., the database CLEAN EATSKITCHEN service as provided by Clean Eats Kitchen of Myrtle Beach, N.C.,the database INSTACART service as provided by Instacart of SanFrancisco, Calif., the database HUNGRY ROOT service as provided byHungry Root of New York, N.Y., the database SUNBASKET service asprovided by Sunbasket of San Francisco, Calif., the database DAILYHARVEST service as provided by Daily Harvest of New York, N.Y., thedatabase GOBBLE service as provided by Gobble of Palo Alto, Calif., thedatabase SPLENDID SPOON service as provided by Splendid Spoon of NewYork, N.Y., and the like thereof. A user aliment history may includequestionnaire and/or survey. As used in this disclosure a“questionnaire” is an organized list questions, examinations, and/orqueries that relate to the user aliment history. For example, andwithout limitation, a user may be asked how many times a week theyconsume chocolate and/or deserts. A user aliment history may includehistory from a databank as a function of a user participation vector. Asused in this disclosure a “user participation vector” is a value thatrelates a user's previous history of participation in nutritionrequirements. For example, and without limitation, a participationvector of 20 may be previously stored relating a strong likelihood for auser to maintain a nutrition requirement, while a participation vectorof 1 previously stored may relate a low likelihood for a user tomaintain a nutrition requirement.

Still referring to FIG. 1, user attribute may be further comprised ofuser input, wherein the user input includes an element relating tohealth conditions. As used in this disclosure “user input” is any datum,value, vector, or element entered to computing device 104. A user inputmay include a user's previous meal selection. As used in this disclosurea “health condition” is the physical or emotional status of the user. Aphysical status, may include, without limitation, normal, good, well,fatigue, lethargic, tired, disease, sick, ill, or the like thereof. Anemotional status of the user may include anger, disgust, fear,happiness, sadness, surprise, neutrality, joy, trust, anticipation,friendship, shame, kindness, pity, indignation, envy, love, suffering,and the like thereof.

With continued reference to FIG. 1, computing device 104 is configuredto identify a nutrition requirement 112 as a function of the userattribute. As used in this disclosure a “nutrition requirement” is arequired nutrient the user needs to consume in order to enhance and/oraid the user attribute. Nutrition requirement may be comprised ofnutrients including, but not limited to, carbohydrates, complexcarbohydrates, lipids, fatty acids, steroids, cholesterols, proteins,amino acids branched chain amino acids, vitamins, minerals,electrolytes, and the like thereof. For example a nutrition requirementmay include, without limitation, 64 grams of protein, 30 grams of fiber,900 mg of vitamin A, 1.2 mg of Thiamin, 1.3 mg of riboflavin, 16 mg ofniacin, 1.3 mg of vitamin B6, 2.4 mg of vitamin B12, 500 mg of folate,45 mg of vitamin C, 1 gram of calcium, 150 mg of iodine, 8 mg of iron,500 mg or magnesium, 3.8 grams of potassium, 460 mg of sodium, 14 mg ofzinc, and 200 mg of carbohydrates. Nutrition requirement is identifiedas a function of a nutrition machine-learning process 116. As used inthis disclosure a “nutrition machine-learning process” is amachine-learning process that automatedly uses training data and/or atraining set to generate an algorithm that will be performed by acomputing device and/or module to produce outputs given data provided asinputs; this is in contrast to a non-machine-learning software programwhere the commands to be executed are determined in advance by a userand written in a programming language. Nutrition machine-learningprocess 116 may consist of any supervised, unsupervised, orreinforcement machine-learning process that computing system 104 may ormay not use in the determination of nutrition requirement 112. Nutritionmachine-learning process 112 may include, without limitation,machine-learning processes such as simple linear regression, multiplelinear regression, polynomial regression, support vector regression,ridge regression, lasso regression, elasticnet regression, decision treeregression, random forest regression, logistic regression, logisticclassification, K-nearest neighbors, support vector machines, kernelsupport vector machines, naïve bayes, decision tree classification,random forest classification, K-means clustering, hierarchicalclustering, dimensionality reduction, principal component analysis,linear discriminant analysis, kernel principal component analysis,Q-learning, State Action Reward State Action (SARSA), Deep-Q network,Markov decision processes, Deep Deterministic Policy Gradient (DDPG), orthe like thereof.

Still referring to FIG. 1, nutrition machine-learning process 116 may becalculated as a function of a nutrition training set 120. As used inthis disclosure “nutrition training set” is a training set thatcorrelates at least an aliment to a user attribute, wherein a userattribute is described above. For example, a user attribute may include,without limitation, fever, influenza, vomiting, nausea, headache,chills, numbness, and the like thereof. As used in this disclosure an“aliment” is a source of nutrition that may be consumed by a user suchthat the user may absorb the nutrients from the source. For example andwithout limitation, an aliment may include legumes, plants, fungi, nuts,seeds, breads, dairy, eggs, meat, cereals, rice, seafood, desserts,dried foods, dumplings, pies, noodles, salads, stews, soups, sauces,sandwiches, and the like thereof. As a non-limiting example nutritiontraining set 120 may relate a user attribute of lethargy with coffeebeans and or bananas, wherein coffee beans provide the nourishment ofcaffeine to increase energy levels and bananas provide the nourishmentof potassium to also increase energy levels. Additionally oralternatively, nutrition machine-learning process 116 may be generatedas a function of a classifier, wherein the classifier may receive theuser affliction of a plurality of user afflictions and output one ormore aliments that are related to at least one or more user afflictions.As used in this disclosure a “classifier” is a machine-learning model,such as a mathematical model, neural net, or program generated by amachine-learning algorithm known as a “classification algorithm,” asdescribed below, that sorts inputs into categories or bins of data,outputting the categories or bins of data and/or labels associatedtherewith. A classifier may be configured to output at least a datumthat labels or otherwise identifies a set of data that are clusteredtogether, found to be close under a distance metric as described below,or the like. Computing device 104 and/or another device may generate aclassifier using a classification algorithm, defined as a processwhereby a computing device 104 derives a classifier from training data.Classification may be performed using, without limitation, linearclassifiers such as without limitation logistic regression and/or naiveBayes classifiers, nearest neighbor classifiers such as k-nearestneighbors classifiers, support vector machines, least squares supportvector machines, fisher's linear discriminant, quadratic classifiers,decision trees, boosted trees, random forest classifiers, learningvector quantization, and/or neural network-based classifiers. Forexample, and without limitation, a classifier may receive an input ofdepressed, wherein an aliment of chocolate may be outputted as chocolatemay provide increased levels of dopamine and may reduce the effects ofdopamine.

Still referring to FIG. 1, computing device 104 may be configured togenerate a classifier using a Naïve Bayes classification algorithm.Naïve Bayes classification algorithm generates classifiers by assigningclass labels to problem instances, represented as vectors of elementvalues. Class labels are drawn from a finite set. Naïve Bayesclassification algorithm may include generating a family of algorithmsthat assume that the value of a particular element is independent of thevalue of any other element, given a class variable. Naïve Bayesclassification algorithm may be based on Bayes Theorem expressed asP(A/B)=P(B/A) P(A)+P(B), where P(AB) is the probability of hypothesis Agiven data B also known as posterior probability; P(B/A) is theprobability of data B given that the hypothesis A was true; P(A) is theprobability of hypothesis A being true regardless of data also known asprior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naïve Bayes algorithm may be generatedby first transforming training data into a frequency table. Computingdevice 104 may then calculate a likelihood table by calculatingprobabilities of different data entries and classification labels.Computing device 104 may utilize a naïve Bayes equation to calculate aposterior probability for each class. A class containing the highestposterior probability is the outcome of prediction. Naïve Bayesclassification algorithm may include a gaussian model that follows anormal distribution. Naïve Bayes classification algorithm may include amultinomial model that is used for discrete counts. Naïve Bayesclassification algorithm may include a Bernoulli model that may beutilized when vectors are binary.

With continued reference to FIG. 1, computing device 104 may beconfigured to generate a classifier using a K-nearest neighbors (KNN)algorithm. A “K-nearest neighbors algorithm” as used in this disclosure,includes a classification method that utilizes feature similarity toanalyze how closely out-of-sample-features resemble training data toclassify input data to one or more clusters and/or categories offeatures as represented in training data; this may be performed byrepresenting both training data and input data in vector forms, andusing one or more measures of vector similarity to identifyclassifications within training data, and to determine a classificationof input data. K-nearest neighbors algorithm may include specifying aK-value, or a number directing the classifier to select the k mostsimilar entries training data to a given sample, determining the mostcommon classifier of the entries in the database, and classifying theknown sample; this may be performed recursively and/or iteratively togenerate a classifier that may be used to classify input data as furthersamples. For instance, an initial set of samples may be performed tocover an initial heuristic and/or “first guess” at an output and/orrelationship, which may be seeded, without limitation, using expertinput received according to any process as described herein. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data. Heuristic mayinclude selecting some number of highest-ranking associations and/ortraining data elements.

With continued reference to FIG. 1, generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute l as derived using aPythagorean norm: l=√{square root over (Σ_(i=0) ^(n)a_(i) ²)}, where ais attribute number i of the vector. Scaling and/or normalization mayfunction to make vector comparison independent of absolute quantities ofattributes, while preserving any dependency on similarity of attributes;this may, for instance, be advantageous where cases represented intraining data are represented by different quantities of samples, whichmay result in proportionally equivalent vectors with divergent values.

Still referring to FIG. 1 computing device 104 is configured to receivea monitoring element 124 from a monitoring device. As used in thisdisclosure a “monitoring element” is a sign, symptom, element, orquality that relates to a user. For instance, monitoring element 124 mayinclude, without limitation, heart rate, calories burned, steps walked,blood pressure, biochemicals detected, time spent exercising, seizures,physical strain, or the like thereof. As a further non-limiting example,monitoring element 124 may include mood quality, anxiety levels, sleepquality, or the like thereof. As used in this disclosure “monitoringdevice” is an electronic device that is worn on the person of a user,such as without limitation close to and/or on the surface of the skin,wherein the device can detect, analyze, and transmit informationconcerning a body signal such as a vital sign, and/or ambient datum,wherein allowing immediate biofeedback to be sent to the user wearingthe device. For example and without limitation, a monitoring device mayinclude, without limitation, any device that further collects, stores,and analyzes data associated with monitoring elements. As a furthernon-limiting example, a monitoring device may consist of near-bodyelectronics, on-body electronics, in-body electronics, electronictextiles, smart watches, smart glasses, smart clothing, fitnesstrackers, body sensors, wearable cameras, head-mounted displays, bodyworn cameras, Bluetooth headsets, wristbands, smart garments, cheststraps, sports watches, fitness monitors, and the like thereof. As afurther non-limiting example, a monitoring device may include earphones,earbuds, headsets, bras, suits, jackets, trousers, shirts, pants, socks,bracelets, necklaces, brooches, rings, jewelry, AR HMDs, VR HMDs,exoskeletons, location trackers, and gesture control wearables. As afurther non-limiting example, a monitoring device may consist of,without limitation an Apple watch, Galaxy watch, FitBit Sense, FossilGen 5, Tag Heuer Connected, Garmin Instinct, and the like thereof.

Still referring to FIG. 1, computing device 104 is configured to modifynutrition requirement 112 such that a modified nutrition requirement 128results as a function of monitoring element 124. As used in thisdisclosure “modified nutrition requirement” is an altered nutritionalnecessity that is generated as a function of the monitoring element. Forexample a nutrition requirement of 120 grams of protein, 15 grams offiber, 800 mg of vitamin A, 3.4 mg of Thiamin, 6.8 mg of riboflavin, 20mg of niacin, 5 mg of vitamin B6, 0.4 mg of vitamin B12, 300 mg offolate, 21 mg of vitamin C, 5 grams of calcium, 100 mg of iodine, 9 mgof iron, 150 mg or magnesium, 5 grams of potassium, 350 mg of sodium, 16mg of zinc, and 500 mg of carbohydrates, wherein a monitoring element ofhigh blood sugar may be obtained, resulting in a modified nutritionrequirement of 20 mg of carbohydrates instead of the 500 mg previouslyidentified. Modified nutrition requirement 128 is generated as afunction of a modification machine-learning process 132. As used in thisdisclosure “modification machine-learning process” is a machine-learningprocess that automatedly uses training data and/or a training set togenerate an algorithm that will be performed by a computing deviceand/or module to produce outputs given data provided as inputs; this isin contrast to a non-machine-learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language. Modification machine-learning process 132 mayconsist of any supervised, unsupervised, or reinforcementmachine-learning process that computing system 104 may or may not use inthe determination of modified nutrition requirement 128. Modificationmachine-learning process 132 may include, without limitation,machine-learning processes such as simple linear regression, multiplelinear regression, polynomial regression, support vector regression,ridge regression, lasso regression, elasticnet regression, decision treeregression, random forest regression, logistic regression, logisticclassification, K-nearest neighbors, support vector machines, kernelsupport vector machines, naïve bayes, decision tree classification,random forest classification, K-means clustering, hierarchicalclustering, dimensionality reduction, principal component analysis,linear discriminant analysis, kernel principal component analysis,Q-learning, State Action Reward State Action (SARSA), Deep-Q network,Markov decision processes, Deep Deterministic Policy Gradient (DDPG), orthe like thereof.

Still referring to FIG. 1, modification machine-learning process 132 Isgenerated as a function of a modification training set 136. As used inthis disclosure a “modification training set” relates at least amonitoring element with a nutrition outcome. As used in this disclosurea “monitoring element” is an element relating to one or more humanphysiological statuses, wherein human physiological statuses may includeheartbeat, blood pressure, body temperature, electrocardiograms,arrhythmias, cancerous indicators, body fat composition, or the likethereof. As a non-limiting example, monitoring elements may include datacollected from using one or more pressure sensors, humidity sensors,position sensors, piezo film sensors, force sensors, temperaturesensors, optical sensors, or the like thereof. As a further non-limitingexample monitoring elements may include data collected from using X-rayabsorptiometry, hydrostatic weighing, air displacement plethysmography,bioelectrical impedance analysis, bioimpedance spectroscopy, electricalimpedance myograph, 3-D scanners, and multi-compartment models. As usedin this disclosure a “nutrition outcome” is a resulting nutritiondeficiency associated with the monitoring element. For example, andwithout limitation a nutrition outcome may include a monitoring elementof decreased red blood cell count, wherein the nutrition outcome maycorrelate to low concentrations of concentrations of iron. As a furthernon-limiting a monitoring element of high blood pressure, may correlateto an increased saturated fat. As a further non-limiting example amonitoring element of decreased sleep may correlate to lowerconcentrations of caffeine and or niacin in a user.

Still referring to FIG. 1, computing device 104 is configured toidentify an aliment 140 that fulfills modified nutrition requirement128, wherein an aliment is discussed in detail above. As a non-limitingexample, computing device 104 may identify an aliment of spinach as afunction of a modified nutrition requirement of low iron. Computingdevice 104 may further hierarchically sort aliments. As used in thisdisclosure a “sorted list” is an ordered collection of data elements forwhich an order of presentation is defined according to ascending ordescending values of a quantitative or other textual field associatedwith each element in the ordered collection. Computing device 104 mayaccomplish this, without limitation, by determining a nourishment valuecorresponding to the modified nutrition requirement. As used in thisdisclosure “nourishment value” is a quantitative value associated withthe nutrients contained in each potential aliment. As a non-limitingexample a nourishment value for salmon may be 90 for omega-3-fattyacids, 30 for protein, 2 for saturated fat, and 0.1 for carbohydrates.Computing device 104 may utilize an individual nourishment value or acombination of nourishment values to determine the sorted aliment list.

Still referring to FIG. 1, computing device 104 may create a distancemetric from the nourishment value to a candidate aliment of a pluralityof candidate aliments and select at least an aliment that minimizes thedistance. As used in this disclosure, a “distance metric” is aquantitative value indicating a degree of similarity of a set of datavalues to another set of data values. For instance, and withoutlimitation, combinations of nourishment values associated with eachcandidate aliment of a plurality of candidate aliments may berepresented a vector. Each vector may be represented, withoutlimitation, as an n-tuple of values, where n is at least two values.Each value of n-tuple of values may represent a measurement or otherquantitative value associated with a given category of data, orattribute, such as a nutrients, examples of which are provided infurther detail below; a vector may be represented, without limitation,in n-dimensional space using an axis per category of value representedin n-tuple of values, such that a vector has a geometric directioncharacterizing the relative quantities of attributes in the n-tuple ascompared to each other. A non-limiting distance metric may include adegree of vector similarity. Two vectors may be considered equivalentwhere their directions, and/or the relative quantities of values withineach vector as compared to each other, are the same; thus, as anon-limiting example, a vector represented as [5, 10, 15] may be treatedas equivalent, for purposes of this disclosure, as a vector representedas [1, 2, 3]. Vectors may be more similar where their directions aremore similar, and more different where their directions are moredivergent, for instance as measured using cosine similarity; however,vector similarity may alternatively or additionally be determined usingaverages of similarities between like attributes, or any other measureof similarity suitable for any n-tuple of values, or aggregation ofnumerical similarity measures for the purposes of loss functions asdescribed in further detail below. Any vectors as described herein maybe scaled, such that each vector represents each attribute along anequivalent scale of values. Each vector may be “normalized,” or dividedby a “length” attribute, such as a length attribute l as derived using aPythagorean norm: l=√{square root over (Σ_(i=0) ^(n)a_(i) ²)}, where ais attribute number i of the vector. Scaling and/or normalization mayfunction to make vector comparison independent of absolute quantities ofattributes, while preserving any dependency on similarity of attributes;this may, for instance, be advantageous where cases represented intraining data are represented by different quantities of samples, whichmay result in proportionally equivalent vectors with divergent values.As a non-limiting illustration, nourishment values from candidatealiments, and/or one or more subsets thereof, may be represented using avector or other data structure, and nutrients provided by each candidatealiment of a plurality of candidate aliments may be represented by alike data structure, such as another vector; a distance metric comparingthe two data structures may then be calculated and compared to distancemetrics calculations to find a minimal distance metric calculationand/or a set of minimal distance metric calculations. A set of minimaldistance metric calculations may be a set of distance metriccalculations less than a preconfigured threshold distance from datastructure representing target nutrients. Preconfigured threshold may beset by one or more expert users and/or determined statistically, forinstance by finding a top quartile and/or number of percentiles ofproximity in a series of distance metric determinations over time foruser, at one time for a plurality of users, and/or over time for aplurality of users. Plurality of users may include a plurality of usersselected by a user classifier, which may classify user to a plurality ofusers having similar physiological data and/or user data; implementationof a user classifier may be performed, without limitation, as describedin U.S. Nonprovisional application Ser. No. 16/865,740, filed on May 4,2020 and entitled “METHODS AND SYSTEMS FOR SYSTEM FOR NUTRITIONALRECOMMENDATION USING ARTIFICIAL INTELLIGENCE ANALYSIS OF IMMUNEIMPACTS,” the entirety of which is incorporated herein by reference. Inan embodiment, a distance metric may include a measurement of anoptimization of one or more factors that include carbohydrates, fats,and/or protein.

Still referring to FIG. 1, computing device 104 is configured to presentaliment 140 on a display device 144. As used in this disclosure a“display device” is an output device for presentation of information invisual or tactile form. As a non-limiting example a display device mayinclude liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, an electroluminescent(ELD) display, a quantum dot (QLED) display, and the like thereof in anycombination. Computing device 104 may display an aliment and/or a sortedlist of aliments for the user.

Referring now to FIG. 2, an exemplary embodiment of a machine-learningmodule 200 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure usingmachine-learning processes. A “machine-learning process,” as used inthis disclosure, is a process that automatedly uses training data 204 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 208 given data provided as inputs 212;this is in contrast to a non-machine-learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 2, “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 204 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 204 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 204 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 204 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 204 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 204 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data204 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 2,training data 204 may include one or more elements that are notcategorized; that is, training data 204 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 204 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 204 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 204 used by machine-learning module 200 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample a user attribute of lethargic may be used as an input, whereinan output may be a nutrition requirement of caffeine.

Further referring to FIG. 2, training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 216. Training data classifier 216 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine-learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 200 may generate aclassifier using a classification algorithm, defined as a processwhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 204. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 216 may classify elements of training data tosub-populations of emotional qualifiers, such as aliments that are in asub-population of energetic, sad, angry, depressed, or the like thereof.

Still referring to FIG. 2, machine-learning module 200 may be configuredto perform a lazy-learning process 220 and/or protocol, which mayalternatively be referred to as a “lazy loading” or “call-when-needed”process and/or protocol, may be a process whereby machine-learning isconducted upon receipt of an input to be converted to an output, bycombining the input and training set to derive the algorithm to be usedto produce the output on demand. For instance, an initial set ofsimulations may be performed to cover an initial heuristic and/or “firstguess” at an output and/or relationship. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data 204. Heuristic may include selecting somenumber of highest-ranking associations and/or training data 204elements. Lazy learning may implement any suitable lazy learningalgorithm, including without limitation a K-nearest neighbors algorithm,a lazy naïve Bayes algorithm, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variouslazy-learning algorithms that may be applied to generate outputs asdescribed in this disclosure, including without limitation lazy learningapplications of machine-learning algorithms as described in furtherdetail below.

Alternatively or additionally, and with continued reference to FIG. 2,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 224. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 224 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 224 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 204set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 2, machine-learning algorithms may include atleast a supervised machine-learning process 228. At least a supervisedmachine-learning process 228, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude insomnia as described above as inputs, nutrition plans withaliments containing high levels of tryptophan as outputs, and a scoringfunction representing a desired form of relationship to be detectedbetween inputs and outputs; scoring function may, for instance, seek tomaximize the probability that a given input and/or combination ofelements inputs is associated with a given output to minimize theprobability that a given input is not associated with a given output.Scoring function may be expressed as a risk function representing an“expected loss” of an algorithm relating inputs to outputs, where lossis computed as an error function representing a degree to which aprediction generated by the relation is incorrect when compared to agiven input-output pair provided in training data 204. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various possible variations of at least a supervisedmachine-learning process 228 that may be used to determine relationbetween inputs and outputs. Supervised machine-learning processes mayinclude classification algorithms as defined above.

Further referring to FIG. 2, machine-learning processes may include atleast an unsupervised machine-learning processes 232. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 2, machine-learning module 200 may be designedand configured to create a machine-learning model 224 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 2, machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring now to FIG. 3, an exemplary embodiment of 300 a user alimenthistory database 304 according to an embodiment of the invention isillustrated. Aliment history database 304 may be implemented, withoutlimitation, as a relational database, a key-value retrieval databasesuch as a NOSQL database, or any other format or structure for use as adatabase that a person skilled in the art would recognize as suitableupon review of the entirety of this disclosure. Database mayalternatively or additionally be implemented using a distributed datastorage protocol and/or data structure, such as a distributed hash tableor the like. Database may include a plurality of data entries and/orrecords as described above. Data entries in a database may be flaggedwith or linked to one or more additional elements of information, whichmay be reflected in data entry cells and/or in linked tables such astables related by one or more indices in a relational database. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which data entries in a database may store,retrieve, organize, and/or reflect data and/or records as used herein,as well as categories and/or populations of data consistently with thisdisclosure. User aliment history database may 304 may include one ormore tables, including without limitation, an online food service tableset 308; online food service tableset 308 may include online foodservice providers that at least provide an aliment or combination ofaliments delivered to a user. As a non-limiting example online foodservice tableset may include, without limitation, UberEats, Instacart,Jet.com, Drizzly, Blue Apron, or the like thereof. User aliment historydatabase 304 may include a participation vector tableset 312.Participation vector tableset 312 may include previous nutritionrequirements that a user has participated in and how effective thenutrition requirement was. As a non-limiting example, participationvector tableset may indicate a user has low participation on diet planssuch as weight watchers and/or the Atkins diet, while a user has a highparticipation in diet plans associated with increased protein such asthe Keto-diet or paleo diet. User aliment history databased may includea questionnaire tableset 316. Questionnaire tableset may indicate auser's previous responses to questions associated with preferences,wants, needs, urges, wishes, or partiality towards aliments and/oraliment nutrition.

Referring now to FIG. 4, an exemplary embodiment of a schematic formodifying a nutrition requirement using a monitoring device. Computingdevice 104 is configured to initiate a communication channel interface404 between computing device 104 and a client device operated by a humansubject. A “human subject,” as used in this disclosure, is a personusing and/or operating a client device. A “communication channelinterface,” as used in this disclosure, is a communication medium withinan interface. Communication channel interface 404 may include anapplication, script, and/or program capable of providing a means ofcommunication between at least two parties, including any oral and/orwritten forms of communication. Communication channel interface 404 mayallow computing device 104 to interface with electronic devices throughgraphical icons, audio indicators including primary notation, text-baseduser interfaces, typed command labels, text navigation, and the like.Communication channel interface 404 may include slides or other commandsthat may allow a user to select one or more options. Communicationchannel interface 404 may include free form textual entries, where auser may type in a response and/or message. Communication channelinterface 404 includes a display interface. A display interface includesa form or other graphical element having display fields, where one ormore elements of information may be displayed. A display interface maydisplay data output fields including text, images, or the likecontaining one or more messages. Communication channel interface 404 mayinclude data input fields such as text entry windows, drop-down lists,buttons, checkboxes, radio buttons, sliders, links, or any other datainput interface that may capture user interaction as may occur topersons skilled in the art upon reviewing the entirety of thisdisclosure. Communication channel interface 404 may be provided, withoutlimitation, using a web browser, a native application, a mobileapplication, and the like.

With continued reference to FIG. 4, computing device 104 initiatescommunication channel interface 404 with a client device 408. A “clientdevice,” as used in this disclosure, is a second computing device,including for example a mobile device such as a smartphone, tablet,laptop, desktop, and/or any other type of device suitable for use ascomputing device. Client device 408 is operated by a human subject 412;human subject 412 may include a person operating a client device.Computing device 104 may initiate communication channel interface 404using any network methodology as described herein. In an embodiment, acommunication channel interface may be utilized to facilitatecommunications between a client device operated by a human subject, andcomputing device which may be operated by a user; user may include asecondary human subject that may provide advice and/or monitor a humansubject's progress. For example, client device 408 may be operated by a30-year-old male who is in communication with an informed advisoroperating computing device. In yet another non-limiting example, clientdevice may be operated by a first member of a fitness group, andcomputing device may be operated by a second member of the fitnessgroup, whereby communication channel interface may be utilized tofacilitate fitness group meetings and secure communications betweenmembers of the fitness group.

With continued reference to FIG. 4, communication channel interface 404includes a monitoring device 416, wherein a “monitoring device,” relatesinformation regarding a physiological status that pertains to client412, such that a modified nutrition requirement may be identified.Monitoring device 416 may be further comprised, but is not limited to, acamera, a video camera, a mobile device, a recording device, a sensorand/or visual capture device, and the like. In an embodiment, amonitoring device may be located within client device, client clothing,client jewelry, client accessories, and the like thereof.

Now referring to FIG. 5, an exemplary embodiment of a method 500 ofmodifying a nutrition requirement. At step 505 a computing device 104receives a user attribute 108. User Attribute 108 includes any of theuser attribute 108 as described above in reference to FIGS. 1-3. Userattribute 108 may include a characteristic uniquely belonging to theuser. As a non-limiting example user attribute 108 may includeparticular traits, qualities, behaviors, habits relating to a user,medical records, vigor statuses, health qualifiers, or the like thereof.For instance, and without limitation, a user attribute may include aprevious diagnosis of COVID-19. As a further non-limiting example a userstatus may include a previous enjoyment of a keto diet.

Still referring to FIG. 5, at step 510, computing device 104 identifiesa nutrition requirement 112. Nutrition requirement 112 includes any ofthe nutrition requirement 112 as described above in reference to FIGS.1-3. Nutrition requirement 112 may include any necessary nutrientsrequired as a function of a user attribute. For instance, and withoutlimitation, a nutrition requirement of omega-3-fatty acids may berequired for the user attribute of ischemic heart disease. Computingdevice 104 may identify nutrition requirement 112 as a function of anutrition machine-learning process 116. Nutrition machine-learningprocess 116 includes any of the nutrition machine-learning process 116as described above, in reference to FIGS. 1-3. For instance, and withoutlimitation, nutrition machine-learning process 116 may include asupervised machine-learning process or an unsupervised machine-learningprocess. Nutrition machine-learning process 1116 may include aclassification process, such as for example naïve Bayes, k-nearestneighbor, decision tree, and/or random forest. Classification processesinclude any of the classification processes as described above inreference to FIGS. 1-3. Nutrition machine-learning process 116 may beconfigured using a nutrition training set 120. Nutrition training set120 includes any of the nutrition training set 120 as described above inreference to FIGS. 1-3. Nutrition training set 120 may include, withoutlimitation aliment correlating to a user attribute, wherein a userattribute is described above. As a non-limiting example nutritiontraining set 120 may relate a user attribute of overstimulation withmelatonin, wherein melatonin reduces the energy levels of a user.

Still referring to FIG. 5, at step 515, computing device 104 receives amonitoring element 124. Monitoring element 124 includes any of themonitoring element 124 as described above in reference to FIGS. 1-3. Forinstance monitoring element 124 may include heartbeat, temperature,respiratory volume, respiratory rate, blood pressure, movement,bioimpedance, and the like thereof. Monitoring element 124 may bereceived as a function of a monitoring device. A monitoring deviceincludes any of the monitoring device as described above in reference toFIGS. 1-3. For instance, and without limitation, a monitoring device mayinclude a device or fabric that a user maintains in close proximity tothe user, such as a clothing, jewelry, or accessory such that the devicecan relate monitoring element 124 to computing device 104.

Still referring to FIG. 5, at step 520, computing device 104 modifiesnutrition requirement 112, resulting in a modified nutrition requirement128. Modified nutrition requirement 128 includes any of the modifiednutrition requirement 128 as described above, in reference to FIGS. 1-3.Modified nutrition requirement 128 may include, without limitation, anupdated nutritional demand as a function of monitoring element 124. Forinstance, and without limitation modified nutrition requirement 128 mayinclude an updated protein consumption as a result of a monitoringelement relating to anemia. Modified nutrition requirement 128 isgenerated as a function of a modification machine-learning process 132.Modification machine-learning process 132 Includes any of themodification machine-learning process 132 As described above, inreference to FIGS. 1-3. For instance, and without limitation, modifiedmachine-learning process 132 may include a supervised machine-learningprocess or an unsupervised machine-learning process. Modificationmachine-learning process 1132 may include a classification process, suchas for example naïve Bayes, k-nearest neighbor, decision tree, and/orrandom forest. Classification processes include any of theclassification processes as described above in reference to FIGS. 1-3.Modification machine-learning process 132 may be configured using amodification training set 136. Modification training set 136 includesany of the modification training set 136 as described above in referenceto FIGS. 1-3. Modification training set 136 may include, withoutlimitation at least a monitoring element that relates to a nutritionoutcome, wherein a monitoring element is an element relating one or morehuman physiological statuses and a nutrition outcome is a resultingnutrition deficiency that relates to a monitoring element. For instanceand without limitation, a monitoring element of rapid heart rate mayrelate to a nutrition outcome of decreased protein in the user.

Still referring to FIG. 5, at step 525, computing device 104 identifiesan aliment 140. Aliment 140 includes any of the aliment 140 as describedabove in reference to FIGS. 1-3. Aliment 140 may include, withoutlimitation, a source of nutrition to be consumed by a user such that theuser obtains the nutrients from the source. For instance, and withoutlimitation, an aliment may include meats, eggs, milk, fruits,vegetables, and the like thereof.

Still referring to FIG. 5, at step 530, computing device 104 presentsaliment 140 on a display device 144. As used in this disclosure displaydevice 144 includes any of the display device 144 as described above, inreference to FIGS. 1-3. Display device 144 may include, withoutlimitation any monitor, screen, or window capable of depictinginformation. Display device 144 may include televisions, computerscreens, mobile screens, projectors, and the like thereof.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 6 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 600 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 600 includes a processor 604 and a memory608 that communicate with each other, and with other components, via abus 612. Bus 612 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 604 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 604 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 604 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC)

Memory 608 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 616 (BIOS), including basic routines that help totransfer information between elements within computer system 600, suchas during start-up, may be stored in memory 608. Memory 608 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 620 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 608 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 600 may also include a storage device 624. Examples of astorage device (e.g., storage device 624) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 624 may be connected to bus 612 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 624 (or one or morecomponents thereof) may be removably interfaced with computer system 600(e.g., via an external port connector (not shown)). Particularly,storage device 624 and an associated machine-readable medium 628 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 600. In one example, software 620 may reside, completelyor partially, within machine-readable medium 628. In another example,software 620 may reside, completely or partially, within processor 604.

Computer system 600 may also include an input device 632. In oneexample, a user of computer system 600 may enter commands and/or otherinformation into computer system 600 via input device 632. Examples ofan input device 632 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 632may be interfaced to bus 612 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 612, and any combinations thereof. Input device 632 mayinclude a touch screen interface that may be a part of or separate fromdisplay 636, discussed further below. Input device 632 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 600 via storage device 624 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 640. A network interfacedevice, such as network interface device 640, may be utilized forconnecting computer system 600 to one or more of a variety of networks,such as network 644, and one or more remote devices 648 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 644,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 620,etc.) may be communicated to and/or from computer system 600 via networkinterface device 640.

Computer system 600 may further include a video display adapter 652 forcommunicating a displayable image to a display device, such as displaydevice 636. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 652 and display device 636 may be utilized incombination with processor 604 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 600 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 612 via a peripheral interface 656. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve systems andmethods according to the present disclosure. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions, and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for presenting an aliment from amodified nourishment scheme, the system comprising a computing device,the computing device configured to: receive a user attribute, whereinthe user attribute comprises a user affliction; identify a nutritionrequirement as a function of the user attribute wherein identifyingfurther comprises: receiving a nutrition training set, wherein thenutrition training set correlates at least an aliment and at least auser attribute; and generating a nutrition requirement as a function ofthe user attribute, the nutrition training set, and a nutritionmachine-learning process; receive a monitoring element; modify thenutrition requirement as a function of the monitoring element, whereinmodifying further comprises; receiving a modification training set,wherein the modification training set includes a plurality of entries,and each entry of the plurality of entries correlates at least amonitoring element and at least nutrition outcome; and generating amodified nutrition requirement as a function of the monitoring element,the modification training set, and a modification machine-learningprocess; identify an aliment that fulfills the modified user nutritionrequirement, wherein identifying an aliment that fulfills the modifieduser nutrition requirement comprises: hierarchically sorting aliments,wherein hierarchically sorting aliments comprises: determining anourishment value corresponding to the modified nutrition requirement;creating a distance metric from the nourishment value to each candidatealiment of a plurality of candidate aliments; selecting at least onecandidate aliment from the plurality of candidate aliments thatminimizes the distance metric; and sorting each of the plurality ofcandidate aliments based on minimizing the distance metric; and presentthe aliment on a display device.
 2. The system of claim 1, wherein theuser attribute further comprises a user aliment history; and whereinidentifying the nutrition requirement further comprises identifying thenutrition requirement as a function of the user aliment history.
 3. Thesystem of claim 2, wherein receiving the user aliment history furthercomprises receiving user aliment history from a food service datastore.4. The system of claim 2, wherein receiving the user aliment historyfurther comprises receiving user aliment history from a questionnaire.5. The system of claim 2, wherein receiving the user aliment historyfurther comprises receiving user aliment history from a databank as afunction of a user engagement vector.
 6. The system of claim 1, whereinreceiving the user attribute further comprises receiving a user vigorstatus, and wherein identifying the nutrition requirement furthercomprises identifying the nutrition requirement as a function of theuser vigor status.
 7. The system of claim 1, wherein identifying thenutrition requirement further comprises receiving a user affliction asan input and outputting an aliment wherein the aliment relates to theuser affliction.
 8. The system of claim 1, wherein creating the distancefurther comprises creating a classifier distance.
 9. A method forpresenting an aliment from a modified nourishment scheme, the methodfurther comprising: receiving, by a computing device, a user attribute,wherein the user attribute comprises a user affliction; identifying, bythe computing device, a nutrition requirement as a function of the userattribute, wherein identifying further comprises: receiving a nutritiontraining set, wherein the nutrition training set correlates at least analiment and at least a user attribute; and generating a nutritionrequirement as a function of the user attribute, the nutrition trainingset, and a nutrition machine-learning process; receiving, by thecomputing device, a monitoring element; modifying, by the computingdevice, the nutrition requirement as a function of the monitoringelement, wherein modifying further comprises; receiving a modificationtraining set, wherein the modification training set includes a pluralityof entries, and each entry of the plurality of entries correlates atleast a monitoring element and at least nutrition outcome; andgenerating a modified nutrition requirement as a function of themonitoring element, the modification training set, and a modificationmachine-learning process; identifying, by the computing device, analiment that fulfills the modified user nutrition requirement, whereinidentifying the aliment that fulfills the modified user nutritionrequirement comprises: hierarchically sorting, by the computing device,aliments, wherein hierarchically sorting aliments comprises:determining, by the computing device, a nourishment value correspondingto the modified nutrition requirement; creating, by the computingdevice, a distance metric from the nourishment value to each candidatealiment of a plurality of candidate aliments; selecting, by thecomputing device, at least one candidate aliment from the plurality ofcandidate aliments that minimizes the distance metric; and sorting, bythe computing device, each of the plurality of candidate aliments basedon minimizing the distance metric; and presenting, by the computingdevice, the aliment on a display device.
 10. The method of claim 9,wherein the user attribute further comprises a user aliment history; andwherein identifying the nutrition requirement further comprisesidentifying the nutrition requirement as a function of the user alimenthistory.
 11. The method of claim 10, wherein receiving the user alimenthistory further comprises receiving user aliment history from a foodservice datastore.
 12. The method of claim 10, wherein receiving theuser aliment history further comprises receiving user aliment historyfrom a questionnaire.
 13. The method of claim 10, wherein receiving theuser aliment history further comprises receiving user aliment historyfrom a databank as a function of a user engagement vector.
 14. Themethod of claim 9, wherein the user attribute further comprises a uservigor status and wherein identifying the nutrition requirement furthercomprises identifying the nutrition requirement as a function of theuser vigor status.
 15. The method of claim 9, wherein identifying thenutrition requirement further comprises receiving a user affliction asan input and outputting an aliment that at least relates to the useraffliction.
 16. The method of claim 9, wherein creating the distancefurther comprises creating a classifier distance.